Discover our solutions

How we help categorize content, organize your terms and concepts, discover hidden information

Solutions for customer support

Consumer feedback and reviews

User comments and reviews pile up on social media or e-commerce web sites and it is hard to keep track of what matters most. Mondeca quickly analyzes user-generated content, detects significant elements to pay attention to and delivers analytics.

How machine learning improves customer services and quality monitoring

CAM serves machine learning models to improve daily consumer service and quality monitoring at an international food and beverage company. Machine learning models help teams with cost-effective automation, targeting and responsiveness.

How it works

CAM models assign risk tags to incoming consumer service flows and alert quality teams in real time, grading problems detected in consumer feedback with high accuracy (98%).

Models are configured and trained directly in CAM. CAM-trained ML models are applied in real time to all incoming flows, including consumer tickets, emails, and posts on social networks. Results are immediately available to customer service and quality monitoring teams via alerts and analytical dashboards.

Enterprise common classification

“We identified the need for a common search terms solution to support improved central data management, semantic tagging and classification of content, as well as dynamic integration of the vocabularies with the platforms in place. “

Common terms and enterprise classification

The City of Toronto relies on controlled vocabularies for content tagging to make it more searchable for their teams and for citizens who connect to their public websites.

The construction and maintenance of taxonomies using Mondeca is rationalized to avoid laborious and manual processes and provide intuitive user interfaces and visual presentations.

The City takes advantage of Mondeca’s workflow capabilities for term suggestion/acceptance/decline to enable governance and crowd-sourcing of new terms in a collaborative environment.
Content tagging also serves as a bridge for integration between taxonomies and a variety of client applications to foster system interoperability, tagging analytics, and metadata reuse.

Deliver the right content to the right audience

“We wish to offer our customers a simple personalized digital experience through the channel of their choice. At present they are experiencing an overload of information that may originate from conflicing sources, be out of date and lacks personnalization. The loss of value is due to lack of standardization, diverse taxonomies and incomplete metadata – the content is not discoverable by the right people” 


Vocabulary browsing

Terms and their definitions are easily accessible for non expert users


Consistent tagging

External and internal documents are consistently tagged


Improved search

Users do not need to know exact keywords to retrieve information


Dynamic vocabulary

Candidate terms are automatically generated

Metadata management for finance

“Metadata management was key  to achive our goal. The improvement of  the  metadata management is a priority to deliver better information to the C-Level decisions makers. We discovered the  need for a  metadata inventory to be globally shared through the organisation and used by all content management solutions (data dictionaries, business glossaries, authority lists) in human and machine-readable formats. We selected the provider of software and professional services to setup a scalable solution for our enterprise metadata management tool and to provide metadata specialists with the knowledge and skills to build, manage and exchange metadata across our organisation, aligned to a common, standardized, metadata model.”

Mondeca solution

  • Implement SaaS enterprise metadata management tool
  • Transfer knowledge and skills to Client metadata specialists
  • Advise and support client’s team in data modelling, taxonomy extraction and machine learning processes
  • Deliver specific interfaces to integrate the metadata management solution with third party software
  • Ingest existing data from legacy metadata solutions
  • Deliver post-implementation support and maintenance

How to auto-generate crosswalks between taxonomies

Bridging vocabularies silos when you need to share information between different applications is time-consuming and requires domain expertise.

Automating the linking process between a variety of data sets, with the right level of expert human review, is enabled in ITM. 

ITM built-in Align workbench uses alignment algorithms to automate the process of identifying crosswalks between different vocabularies. Results are scored and typed and can be conveniently analyzed and reviewed using dedicated UIs. ITM Align is therefore adapted to the creation and management of several alignment projects and to alignment review by human agents. Crosswalks become part of your data and can be further used by tagging and search engines to “translate” tags from one terminology to another.

How to identify and extract new terms from a collection of documents

Taxonomies are living structures that must be maintained and managed going forward and can be further improved by monitoring new terms – i.e., candidate terms – identified in documents.

Mondeca’s auto-tagger – CAM – can automatically suggest these candidate terms (concepts or named entities) as it analyzes topics found in one or several documents. CAM uses built-in terminology extraction algorithms based on NLP and ML to list new terms together with relevance scores. Results can be conveniently saved as Excel spreadsheets for further analysis and cleanup, or directly shared with ITM. ITM  is  designed to search, filter, and manage imported candidate terms suggested for inclusion in the taxonomies to better support the tagging process in return.

Book a demo of Mondeca


Let’s talk about your project needs & goals

We will share with you how we can rapidly increase the performance and value of your taxonomy.

  • Discuss your use cases and challenges
  • Show relevant features and capabilities
  • Agree on next steps

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